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Open AccessArticle

Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques

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Aeronautics Advanced Manufacturing Center, CFAA (UPV/EHU), Bizkaia Technology Park, Building 202, 48170 Zamudio, Spain
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Machine-Tool Institute (IMH), Azkue Auzoa 1 48, 20870 Elgoibar, Spain
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iCub Facility, Istituto Italiano di Tecnologia Via Morego, 30, 16163 Genova, Italy
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Faculty of Mechanical Engineering, Tianjin University of Science & Technology (TUST), Dongjiang Rd, Hexi Qu, Tianjin 300222, China
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Faculty of Engineering of Bilbao, UPV/EHU, Plaza Torres Quevedo 1, 48013 Bilbao, Spain
*
Author to whom correspondence should be addressed.
Materials 2018, 11(7), 1100; https://doi.org/10.3390/ma11071100
Received: 13 June 2018 / Revised: 25 June 2018 / Accepted: 26 June 2018 / Published: 28 June 2018
(This article belongs to the Special Issue Special Issue of the Manufacturing Engineering Society (MES))
Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future. View Full-Text
Keywords: WEDM; deep learning; deep neural networks; Industry 4.0 WEDM; deep learning; deep neural networks; Industry 4.0
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MDPI and ACS Style

Sanchez, J.A.; Conde, A.; Arriandiaga, A.; Wang, J.; Plaza, S. Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques. Materials 2018, 11, 1100.

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